This section first introduces our idea of modeling a CNN based on network science [1]}. To this end, we define a group of consecutive layers with the same width (i.e., number of output channels, \(w_c\) ) as a cell; then we break the entire network into multiple cells and denote the number of cells as \(N_c\) . Similar to MobileNet-v2 [2]}, we also adopt a width multiplier (\(w_m\) ) to scale the width of each cell.
Moreover, following most of the mainstream CNN architectures, we assume that each cell inside a CNN has the same number of layers (\(d_c\) ). Furthermore, as shown in Fig. REF , we consider each channel of the feature map as a node in a network and consider each convolution filter/kernel as an undirected link. These notations are summarized in Table REF .
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